Deep learning in forestry using uav-acquired rgb data: A practical review

Y Diez, S Kentsch, M Fukuda, MLL Caceres… - Remote Sensing, 2021 - mdpi.com
Forests are the planet's main CO 2 filtering agent as well as important economical,
environmental and social assets. Climate change is exerting an increased stress, resulting …

Review of wildfire modeling considering effects on land surfaces

D Or, E Furtak-Cole, M Berli, R Shillito… - Earth-Science …, 2023 - Elsevier
Wildfires are part of the natural cycle of life in vegetated regions. The apparent increase in
size and frequency of recent years reflects land management legacy, expansion of human …

[PDF][PDF] Agriculture, forestry and other land uses (AFOLU)

GJ Nabuurs, R Mrabet, AA Hatab… - Climate Change 2022 …, 2023 - library.wur.nl
Executive Summary The Agriculture, Forestry and Other Land Use1 (AFOLU) sector
encompasses managed ecosystems and offers significant mitigation opportunities while …

Global population exposure to landscape fire air pollution from 2000 to 2019

R Xu, T Ye, X Yue, Z Yang, W Yu, Y Zhang, ML Bell… - Nature, 2023 - nature.com
Wildfires are thought to be increasing in severity and frequency as a result of climate
change,,,–. Air pollution from landscape fires can negatively affect human health,–, but …

Wildland fire detection and monitoring using a drone-collected RGB/IR image dataset

X Chen, B Hopkins, H Wang, L O'Neill, F Afghah… - IEEE …, 2022 - ieeexplore.ieee.org
Current forest monitoring technologies including satellite remote sensing, manned/piloted
aircraft, and observation towers leave uncertainties about a wildfire's extent, behavior, and …

Multi-decadal trends and variability in burned area from the 5th version of the Global Fire Emissions Database (GFED5)

Y Chen, J Hall, D Van Wees, N Andela… - Earth System …, 2023 - essd.copernicus.org
Long-term records of burned area are needed to understand wildfire dynamics, assess fire
impacts on ecosystems and air quality, and improve fire forecasts. Here we fuse multiple …

Next day wildfire spread: A machine learning dataset to predict wildfire spreading from remote-sensing data

F Huot, RL Hu, N Goyal, T Sankar… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Predicting wildfire spread is critical for land management and disaster preparedness. To this
end, we present “Next Day Wildfire Spread,” a curated, large-scale, multivariate dataset of …

Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India

AL Achu, J Thomas, CD Aju, G Gopinath, S Kumar… - Ecological …, 2021 - Elsevier
The recurrent forest fires have been a serious management concern in southern Western
Ghats, India. This study investigates the applicability of various geospatial data, machine …

A deep learning ensemble model for wildfire susceptibility mapping

A Bjånes, R De La Fuente, P Mena - Ecological Informatics, 2021 - Elsevier
Devastating wildfires have increased in frequency and intensity over the last few years,
worsened by climate change and prolonged droughts. Wildfire susceptibility mapping with …

Remote sensing of forest burnt area, burn severity, and post-fire recovery: A review

E Kurbanov, O Vorobev, S Lezhnin, J Sha, J Wang… - Remote Sensing, 2022 - mdpi.com
Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and
their sustainability. In addition, they have a strong impact on the global carbon balance and …